Overview

Dataset statistics

Number of variables14
Number of observations204254
Missing cells196561
Missing cells (%)6.9%
Duplicate rows267
Duplicate rows (%)0.1%
Total size in memory21.8 MiB
Average record size in memory112.0 B

Variable types

Numeric11
Categorical3

Alerts

Dataset has 267 (0.1%) duplicate rowsDuplicates
COD_PRIORIDADE is highly correlated with AFLUENCIAHigh correlation
AFLUENCIA is highly correlated with COD_PRIORIDADEHigh correlation
COD_PERG is highly correlated with COD_VIA_VERDEHigh correlation
COD_VIA_VERDE is highly correlated with COD_PERGHigh correlation
QUADRO is highly correlated with COD_PERG and 1 other fieldsHigh correlation
COD_PERG is highly correlated with QUADRO and 1 other fieldsHigh correlation
HORA_ADMISSAO is highly correlated with AFLUENCIAHigh correlation
COD_VIA_VERDE is highly correlated with QUADRO and 2 other fieldsHigh correlation
AFLUENCIA is highly correlated with HORA_ADMISSAOHigh correlation
Internamento is highly correlated with COD_VIA_VERDEHigh correlation
COD_VIA_VERDE has 196505 (96.2%) missing values Missing
COD_PRIORIDADE is highly skewed (γ1 = 20.91813947) Skewed

Reproduction

Analysis started2023-06-05 20:47:22.807351
Analysis finished2023-06-05 20:47:56.368774
Duration33.56 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

URG_EPISODIO
Real number (ℝ≥0)

Distinct199485
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21556822.94
Minimum21000001
Maximum22167405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:56.476145image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum21000001
5-th percentile21016591.3
Q121082665.75
median21171830
Q322078642.75
95-th percentile22148454.35
Maximum22167405
Range1167404
Interquartile range (IQR)995977

Descriptive statistics

Standard deviation499021.4174
Coefficient of variation (CV)0.02314911705
Kurtosis-1.946416125
Mean21556822.94
Median Absolute Deviation (MAD)162038.5
Skewness0.113569111
Sum4.403067313 × 1012
Variance2.49022375 × 1011
MonotonicityIncreasing
2023-06-05T21:47:56.586031image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2104966211
 
< 0.1%
210993078
 
< 0.1%
210509905
 
< 0.1%
211562645
 
< 0.1%
220206774
 
< 0.1%
211762724
 
< 0.1%
220119694
 
< 0.1%
220447754
 
< 0.1%
220908124
 
< 0.1%
210016184
 
< 0.1%
Other values (199475)204201
> 99.9%
ValueCountFrequency (%)
210000011
< 0.1%
210000021
< 0.1%
210000041
< 0.1%
210000051
< 0.1%
210000071
< 0.1%
210000081
< 0.1%
210000091
< 0.1%
210000111
< 0.1%
210000121
< 0.1%
210000131
< 0.1%
ValueCountFrequency (%)
221674051
< 0.1%
221674011
< 0.1%
221673991
< 0.1%
221673781
< 0.1%
221673691
< 0.1%
221673651
< 0.1%
221673641
< 0.1%
221673591
< 0.1%
221673571
< 0.1%
221673451
< 0.1%

QUADRO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.41482174
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:56.848354image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q121
median32
Q344
95-th percentile48
Maximum55
Range54
Interquartile range (IQR)23

Descriptive statistics

Standard deviation12.72888935
Coefficient of variation (CV)0.4051873813
Kurtosis-1.015125846
Mean31.41482174
Median Absolute Deviation (MAD)12
Skewness-0.2869730189
Sum6416603
Variance162.0246242
MonotonicityNot monotonic
2023-06-05T21:47:56.949916image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
4442232
20.7%
1620393
10.0%
3720242
9.9%
2118393
 
9.0%
2514483
 
7.1%
2711117
 
5.4%
79371
 
4.6%
327895
 
3.9%
516164
 
3.0%
474953
 
2.4%
Other values (38)49011
24.0%
ValueCountFrequency (%)
11043
 
0.5%
2101
 
< 0.1%
3132
 
0.1%
41
 
< 0.1%
79371
4.6%
81798
 
0.9%
91176
 
0.6%
10783
 
0.4%
1119
 
< 0.1%
1211
 
< 0.1%
ValueCountFrequency (%)
551429
 
0.7%
54357
 
0.2%
516164
 
3.0%
50823
 
0.4%
49480
 
0.2%
483924
 
1.9%
474953
 
2.4%
461614
 
0.8%
451957
 
1.0%
4442232
20.7%

COD_PERG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct148
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.11532699
Minimum2
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:57.053528image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile39
Q143
median52
Q3118
95-th percentile173
Maximum220
Range218
Interquartile range (IQR)75

Descriptive statistics

Standard deviation49.66943595
Coefficient of variation (CV)0.6440929175
Kurtosis-0.2469383964
Mean77.11532699
Median Absolute Deviation (MAD)9
Skewness1.038331002
Sum15751114
Variance2467.052868
MonotonicityNot monotonic
2023-06-05T21:47:57.153725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4363434
31.1%
5233832
16.6%
1537981
 
3.9%
497738
 
3.8%
1097178
 
3.5%
456673
 
3.3%
1316220
 
3.0%
1405668
 
2.8%
1525564
 
2.7%
554867
 
2.4%
Other values (138)55099
27.0%
ValueCountFrequency (%)
2315
 
0.2%
38
 
< 0.1%
4437
 
0.2%
5528
 
0.3%
646
 
< 0.1%
7198
 
0.1%
82868
1.4%
9149
 
0.1%
10193
 
0.1%
11180
 
0.1%
ValueCountFrequency (%)
22016
 
< 0.1%
219117
 
0.1%
21887
 
< 0.1%
21711
 
< 0.1%
214213
 
0.1%
2121277
0.6%
210278
 
0.1%
20915
 
< 0.1%
2088
 
< 0.1%
206533
0.3%

HORA_ADMISSAO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct68474
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51410.18774
Minimum0
Maximum86398
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:57.258920image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12410.3
Q138061.25
median52298.5
Q366978
95-th percentile80715
Maximum86398
Range86398
Interquartile range (IQR)28916.75

Descriptive statistics

Standard deviation19645.07471
Coefficient of variation (CV)0.3821241582
Kurtosis-0.2399878296
Mean51410.18774
Median Absolute Deviation (MAD)14423.5
Skewness-0.4203965073
Sum1.050073649 × 1010
Variance385928960.5
MonotonicityNot monotonic
2023-06-05T21:47:57.358575image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5204615
 
< 0.1%
3481815
 
< 0.1%
3797815
 
< 0.1%
5362914
 
< 0.1%
5210814
 
< 0.1%
3622913
 
< 0.1%
5376713
 
< 0.1%
5055013
 
< 0.1%
3535513
 
< 0.1%
4863313
 
< 0.1%
Other values (68464)204116
99.9%
ValueCountFrequency (%)
01
 
< 0.1%
12
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
102
< 0.1%
122
< 0.1%
133
< 0.1%
ValueCountFrequency (%)
863984
< 0.1%
863972
< 0.1%
863962
< 0.1%
863951
 
< 0.1%
863941
 
< 0.1%
863933
< 0.1%
863921
 
< 0.1%
863911
 
< 0.1%
863891
 
< 0.1%
863884
< 0.1%

COD_CAUSA
Real number (ℝ≥0)

Distinct25
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.315501895
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:57.452728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q35
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.125882227
Coefficient of variation (CV)0.6532944326
Kurtosis145.1326072
Mean6.315501895
Median Absolute Deviation (MAD)0
Skewness8.036385586
Sum1289916
Variance17.02290415
MonotonicityNot monotonic
2023-06-05T21:47:57.537289image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5152096
74.5%
1023323
 
11.4%
127813
 
3.8%
25925
 
2.9%
94332
 
2.1%
12615
 
1.3%
41280
 
0.6%
261082
 
0.5%
221069
 
0.5%
191066
 
0.5%
Other values (15)3645
 
1.8%
ValueCountFrequency (%)
12615
 
1.3%
25925
 
2.9%
3530
 
0.3%
41280
 
0.6%
5152096
74.5%
656
 
< 0.1%
94332
 
2.1%
1023323
 
11.4%
112
 
< 0.1%
127813
 
3.8%
ValueCountFrequency (%)
99113
 
0.1%
303
 
< 0.1%
28223
 
0.1%
261082
0.5%
25393
 
0.2%
241
 
< 0.1%
221069
0.5%
21433
0.2%
20608
0.3%
191066
0.5%

COD_PROVENIENCIA
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing33
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.34530239
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:57.618950image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q15
median5
Q333
95-th percentile33
Maximum33
Range32
Interquartile range (IQR)28

Descriptive statistics

Standard deviation13.1043158
Coefficient of variation (CV)0.8539626958
Kurtosis-1.660751777
Mean15.34530239
Median Absolute Deviation (MAD)0
Skewness0.5546154045
Sum3133833
Variance171.7230926
MonotonicityNot monotonic
2023-06-05T21:47:57.690513image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
5109480
53.6%
3364983
31.8%
711926
 
5.8%
308971
 
4.4%
96127
 
3.0%
152202
 
1.1%
2499
 
0.2%
316
 
< 0.1%
16
 
< 0.1%
104
 
< 0.1%
Other values (6)7
 
< 0.1%
(Missing)33
 
< 0.1%
ValueCountFrequency (%)
16
 
< 0.1%
2499
 
0.2%
316
 
< 0.1%
42
 
< 0.1%
5109480
53.6%
61
 
< 0.1%
711926
 
5.8%
96127
 
3.0%
104
 
< 0.1%
152202
 
1.1%
ValueCountFrequency (%)
3364983
31.8%
321
 
< 0.1%
308971
 
4.4%
281
 
< 0.1%
271
 
< 0.1%
161
 
< 0.1%
152202
 
1.1%
104
 
< 0.1%
96127
 
3.0%
711926
 
5.8%

COD_PRIORIDADE
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct6
Distinct (%)< 0.1%
Missing15
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.279163137
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:57.767363image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile4
Maximum98
Range97
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.402355997
Coefficient of variation (CV)1.34252424
Kurtosis447.1704827
Mean3.279163137
Median Absolute Deviation (MAD)0
Skewness20.91813947
Sum669733
Variance19.38073833
MonotonicityNot monotonic
2023-06-05T21:47:57.836420image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3109930
53.8%
453557
26.2%
237458
 
18.3%
51473
 
0.7%
11392
 
0.7%
98429
 
0.2%
(Missing)15
 
< 0.1%
ValueCountFrequency (%)
11392
 
0.7%
237458
 
18.3%
3109930
53.8%
453557
26.2%
51473
 
0.7%
98429
 
0.2%
ValueCountFrequency (%)
98429
 
0.2%
51473
 
0.7%
453557
26.2%
3109930
53.8%
237458
 
18.3%
11392
 
0.7%

IDADE
Real number (ℝ≥0)

Distinct106
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.15790633
Minimum0
Maximum109
Zeros249
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:57.927436image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q135
median53
Q370
95-th percentile87
Maximum109
Range109
Interquartile range (IQR)35

Descriptive statistics

Standard deviation22.84906763
Coefficient of variation (CV)0.4380748621
Kurtosis-0.855062268
Mean52.15790633
Median Absolute Deviation (MAD)18
Skewness-0.1693593515
Sum10653461
Variance522.0798913
MonotonicityNot monotonic
2023-06-05T21:47:58.031634image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
563364
 
1.6%
553363
 
1.6%
493313
 
1.6%
503293
 
1.6%
513284
 
1.6%
543267
 
1.6%
573253
 
1.6%
603225
 
1.6%
583213
 
1.6%
523195
 
1.6%
Other values (96)171484
84.0%
ValueCountFrequency (%)
0249
 
0.1%
1793
0.4%
2746
0.4%
3616
0.3%
4475
0.2%
5457
0.2%
6549
0.3%
7601
0.3%
8631
0.3%
9757
0.4%
ValueCountFrequency (%)
1093
 
< 0.1%
1045
 
< 0.1%
1037
 
< 0.1%
1026
 
< 0.1%
10112
 
< 0.1%
10058
 
< 0.1%
9971
 
< 0.1%
98150
0.1%
97169
0.1%
96266
0.1%

COD_VIA_VERDE
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing196505
Missing (%)96.2%
Memory size1.6 MiB
2.0
5187 
1.0
2562 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.05187
 
2.5%
1.02562
 
1.3%
(Missing)196505
96.2%

Length

2023-06-05T21:47:58.123676image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-06-05T21:47:58.199767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
2.05187
66.9%
1.02562
33.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SEXO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2
105905 
1
98349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2105905
51.8%
198349
48.2%

Length

2023-06-05T21:47:58.252779image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-06-05T21:47:58.305331image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
2105905
51.8%
198349
48.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

COD_CONCELHO
Real number (ℝ≥0)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.692182283
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:58.355509image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q310
95-th percentile11
Maximum24
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.390017096
Coefficient of variation (CV)0.4407094075
Kurtosis-0.6025782737
Mean7.692182283
Median Absolute Deviation (MAD)2
Skewness-0.4355549112
Sum1571159
Variance11.49221591
MonotonicityNot monotonic
2023-06-05T21:47:58.437599image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1044731
21.9%
1139305
19.2%
526689
13.1%
924594
12.0%
718453
9.0%
114023
 
6.9%
311796
 
5.8%
46860
 
3.4%
25798
 
2.8%
65619
 
2.8%
Other values (14)6386
 
3.1%
ValueCountFrequency (%)
114023
 
6.9%
25798
 
2.8%
311796
 
5.8%
46860
 
3.4%
526689
13.1%
65619
 
2.8%
718453
9.0%
8724
 
0.4%
924594
12.0%
1044731
21.9%
ValueCountFrequency (%)
242
 
< 0.1%
2336
 
< 0.1%
223
 
< 0.1%
2122
 
< 0.1%
2010
 
< 0.1%
1925
 
< 0.1%
1886
 
< 0.1%
17613
0.3%
16148
 
0.1%
151149
0.6%

AFLUENCIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct158
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.3439443
Minimum0
Maximum164
Zeros1785
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:58.682925image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q127
median51
Q369
95-th percentile92
Maximum164
Range164
Interquartile range (IQR)42

Descriptive statistics

Standard deviation26.47840032
Coefficient of variation (CV)0.5366089132
Kurtosis-0.6656648834
Mean49.3439443
Median Absolute Deviation (MAD)20
Skewness0.1147599557
Sum10078698
Variance701.1056838
MonotonicityNot monotonic
2023-06-05T21:47:58.780853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
582943
 
1.4%
622931
 
1.4%
512907
 
1.4%
632901
 
1.4%
642891
 
1.4%
612849
 
1.4%
662847
 
1.4%
522844
 
1.4%
572839
 
1.4%
542834
 
1.4%
Other values (148)175468
85.9%
ValueCountFrequency (%)
01785
0.9%
1953
0.5%
2828
0.4%
3850
0.4%
4915
0.4%
51060
0.5%
61135
0.6%
71311
0.6%
81524
0.7%
91654
0.8%
ValueCountFrequency (%)
1642
< 0.1%
1633
< 0.1%
1621
 
< 0.1%
1571
 
< 0.1%
1551
 
< 0.1%
1541
 
< 0.1%
1531
 
< 0.1%
1513
< 0.1%
1501
 
< 0.1%
1491
 
< 0.1%

LOS
Real number (ℝ≥0)

Distinct57579
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22178.23181
Minimum348
Maximum2851218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-06-05T21:47:58.883549image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum348
5-th percentile3083
Q17807
median16003
Q328184
95-th percentile66080.4
Maximum2851218
Range2850870
Interquartile range (IQR)20377

Descriptive statistics

Standard deviation22755.31125
Coefficient of variation (CV)1.026020083
Kurtosis1181.498907
Mean22178.23181
Median Absolute Deviation (MAD)9364
Skewness11.91213607
Sum4529992561
Variance517804190.2
MonotonicityNot monotonic
2023-06-05T21:47:58.983277image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421222
 
< 0.1%
663020
 
< 0.1%
518820
 
< 0.1%
541319
 
< 0.1%
456219
 
< 0.1%
568519
 
< 0.1%
356719
 
< 0.1%
815919
 
< 0.1%
495119
 
< 0.1%
690219
 
< 0.1%
Other values (57569)204059
99.9%
ValueCountFrequency (%)
3481
< 0.1%
3741
< 0.1%
3781
< 0.1%
3831
< 0.1%
3871
< 0.1%
4071
< 0.1%
4081
< 0.1%
4181
< 0.1%
4681
< 0.1%
5121
< 0.1%
ValueCountFrequency (%)
28512181
< 0.1%
3793821
< 0.1%
3665321
< 0.1%
3481031
< 0.1%
3473351
< 0.1%
3455081
< 0.1%
3246601
< 0.1%
3241601
< 0.1%
3226021
< 0.1%
3205651
< 0.1%

Internamento
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
183867 
1
20387 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0183867
90.0%
120387
 
10.0%

Length

2023-06-05T21:47:59.081842image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2023-06-05T21:47:59.135844image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
0183867
90.0%
120387
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

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2023-06-05T21:47:53.376127image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2023-06-05T21:47:59.212995image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-06-05T21:47:59.360753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-06-05T21:47:59.505533image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-06-05T21:47:59.638351image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-06-05T21:47:59.732161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-06-05T21:47:55.377766image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-05T21:47:55.699079image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-05T21:47:56.070033image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-06-05T21:47:56.175664image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

URG_EPISODIOQUADROCOD_PERGHORA_ADMISSAOCOD_CAUSACOD_PROVENIENCIACOD_PRIORIDADEIDADECOD_VIA_VERDESEXOCOD_CONCELHOAFLUENCIALOSInternamento
02100000125399845.0NaN2.058NaN111052236.00
1210000023710911005.033.03.087NaN26115460.00
22100000432131156312.05.03.012NaN11127737.00
3210000053714416425.033.02.074NaN21185298.00
421000007743296110.030.03.066NaN111319719.00
5210000084443310410.05.03.040NaN2646196.00
621000009216833135.030.02.024NaN21012207.00
721000011473846235.05.04.019NaN2964677.00
82100001243131483810.030.03.03NaN1661642.00
9210000131615371605.05.02.069NaN13143060.00

Last rows

URG_EPISODIOQUADROCOD_PERGHORA_ADMISSAOCOD_CAUSACOD_PROVENIENCIACOD_PRIORIDADEIDADECOD_VIA_VERDESEXOCOD_CONCELHOAFLUENCIALOSInternamento
204244221673454443459989.05.03.016NaN19264282.00
204245221673574443476119.05.03.010NaN21193929.00
204246221673592152478275.05.04.056NaN110236053.00
2042472216736444434828110.05.03.069NaN25204639.00
2042482216736549494841114.05.02.022NaN21016429.00
204249221673694455488375.05.04.028NaN15235403.00
204250221673783752500865.05.04.023NaN25192234.00
2042512216739955140519855.033.04.023NaN26163695.00
2042522216740144435204112.05.03.023NaN110122919.00
204253221674053943522501.033.03.047NaN25132530.00

Duplicate rows

Most frequently occurring

URG_EPISODIOQUADROCOD_PERGHORA_ADMISSAOCOD_CAUSACOD_PROVENIENCIACOD_PRIORIDADEIDADECOD_VIA_VERDESEXOCOD_CONCELHOAFLUENCIALOSInternamento# duplicates
452105099037184298935.033.02.0811.0131111807.005
101211256302746649855.07.02.0652.02112117755.003
125211526872746770985.033.02.0592.0115717402.003
166220060872746529205.05.02.0592.0211732100.013
258221561272746551525.05.02.0722.02118181548.003
0210026392746480395.033.02.0472.01101679761.002
121005100378685895.033.02.0771.0251615831.002
22100655337184554005.033.02.0601.01112420140.012
3210067852746371165.02.02.0632.013728764.012
4210081542746187995.033.02.0922.0272040781.002